Printed Reference Material

For beginners, we highly recommend reading Chapter 1 of this book by Peter S. Maybeck on the subjects of stochastic models, estimation, and control. Although the remaining chapters may appear daunting, the book is thorough and complete.

Software

OpenCV includes some Kalman Filter functions, and the Reference Manual includes some introductory prose. (The prose is quite similar to our introductory paper.) The entire library can be downloaded after agreeing to their license. The Reference Manual is in the opencv-doc package.

Alex Blocker at Boston University has developed and made available some Matlab tools for Kalman filtering, smoothing, and estimation. (See his web site for notes, instructions, and a link to the tools.) The tools are licensed under LGPL 3.0. He says that the learning algorithm he uses is similar to Kevin Murphy's, but is extended to the case of a controlled system. He includes a technical note on this algorithm and its use.

Conor Dolan has made available an executable, limited source, and documentation for a program to carry out state-space modeling based on Harvey's text. It includes facilities to model multi-subject data and multi-group data. You can get a zip file from his web site (see mkfm6 program). Note that Conor says "The source is limited to parts I wrote (in fortran). Optimization of the normal theory ML function is carried out by the NPSOL routine, which I am not at liberty to disseminate."

Jan Kybic's 1998 research report Kalman Filtering and Speech Enhancement details his work aimed at suppression of noise in a running car environment for hands-free mobile telephony. (Thanks to Richard Kilgour at Navman for suggesting this.)

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